A new class of unbiased linear estimators in systematic sampling | Zendy

Eda Gizem Koçyiğit | Zendy; Hülya Çıngı | Zendy

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A new class of unbiased linear estimators in systematic sampling

Author(s) -

Eda Gizem Koçyiğit,

Hülya Çıngı

Publication year - 2017

Publication title -

hacettepe journal of mathematics and statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.312

H-Index - 26

ISSN - 1303-5010

DOI - 10.15672/hjms.2017.413

Subject(s) - mathematics , best linear unbiased prediction , estimator , class (philosophy) , bias of an estimator , statistics , systematic sampling , unbiased estimation , u statistic , sampling (signal processing) , stein's unbiased risk estimate , minimum variance unbiased estimator , selection (genetic algorithm) , artificial intelligence , filter (signal processing) , computer vision , computer science

Use of auxiliary variables is very common in estimating various population parameters. In this study, we suggest a class of unbiased linear estimators for estimating the population mean of the study variate y using information on the auxiliary variate x in systematic sampling. The variance expressions of the suggested estimators are compared with usual unbiased estimator, Swain's (1964) ratio estimator and Shukla's (1971) product type estimator. It is demonstrated that the proposed estimators are more efficient than others.

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